In recent years, data-driven control approaches have been increasingly studied, e.g., [1]}, [2]}, [3]}, [4]}, [5]}. These methods typically consist of a machine learning component that constructs a
a variety of regression models, e.g., polynomial functions and neural networks.
Among them, Gaussian process (GP) regression [6]} offers a unique advantage of providing quantified error bounds between the regressed model and the underlying system [7]}, [8]}, [9]}, [10]}. This has given rise to a wide use of GPs in safe learning frameworks [2]}, [3]}, [4]}. In particular, safe reinforcement learning approaches are developed to take advantage of the GP modeling to learn a control policy with guarantees such as stability [2]}, [5]} and respecting safety constraints [3]}, [4]}.
A shortcoming of classical GP regression, however, is that it leads to computational intractability as the data grows, making it difficult to use for online refinement of policies.
